… Unlike most previous papers on domain adaptation that worked with fixed feature … domain
adaptation and deep feature learning within one training process (deep domain adaptation). …

… in unsupervised domain adaptation. One of the effective methods for unsupervised domain
adaptation are … Each domain has unique characteristics of their features, which decrease the …

… This paper addresses unsupervised domain adaptation within deep networks for jointly
learning transferable features and adaptive classifiers. We extend deep convolutional networks (…

… In this paper, we propose a novel unsupervised domain adaptation algorithm based on
deep learning for visual object recognition. Specifically, we design a new model called Deep …

… -domain discrepancy between the source and target domains, many domain adaptation
methods tried their best to extract domain… the purpose of unsupervised domain adaptation. In a …

… To counter this, unsupervised domain adaptation (UDA) is proposed as a viable solution
to migrate knowledge learned from a labeled source domain to unseen, heterogeneous, and …

… Unsupervised adaptation from MNIST to SVHN gives a failure example for this approach. …
Semi-supervised domain adaptation, ie when one is additionally provided with a small …

… the unsupervised domain adaptation … unsupervised domain adaptation from the convolutional
perspective and develop an attention transfer process for convolutional domain adaptation…

… of the deep learning methods that have been designed for unsupervised domain adaptation
[152… approaches to single-source homogeneous unsupervised deep domain adaptation. …

… unsupervised domain adaptation based on hashing (DAH) that incorporates unsupervised
domain adaptation … hashing for the source (5) and unsupervised hashing for the target (7) in a …